A CT-based deep learning model for subsolid pulmonary nodules to distinguish minimally invasive adenocarcinoma and invasive adenocarcinoma. Issue 145 (December 2021)

• Record Type: Journal Article
• Authors: Chen, Xiangmeng; Feng, Bao; Chen, Yehang; Duan, Xiaobei; Liu, Kunfeng; Li, Kunwei; Zhang, Chaotong; Liu, Xueguo; Long, Wansheng
• Is Part Of: European journal of radiology. Issue 145(2021)
• Publication Date: 2021-12
• Subjects: AAH Atypical adenomatous hyperplasia -- AEC Automatic exposure control -- AIS Adenocarcinoma in situ -- AUC Area under curve -- CI Conference interval -- CNN Convolution neural network -- CT Computed tomography -- DL Deep learning -- DLN Deep learning nomogram -- DLS Deep learning signature -- DCA Decision curve analysis -- FS Frozen section -- HU Hounsfield unit -- IAC Invasive adenocarcinoma -- LASSO Least absolute shrinkage and selection operator -- MIA Minimally invasive adenocarcinoma -- NPV Negative predictive value -- NRI Net reclassification index -- OR Odds ratio -- PACS Picture archiving and communication system -- PPV Positive predictive value -- ROC Receiver operating characteristic -- ROI Region of interest -- SSPNs Subsolid pulmonary nodules; Deep learning -- Nomogram -- Subsolid pulmonary nodule -- Lung adenocarcinoma -- Convolutional neural network; Medical radiology -- Periodicals; Radiology -- Periodicals; Radiologie médicale -- Périodiques; Medical radiology; Periodicals; 616.075705
• DOI: 10.1016/j.ejrad.2021.110041
• Languages: English
• ISSNs: 0720-048X

Abstract

Highlights: The DLN was developed to differentiate MIA from IAC in patients with SSPNs. The DLN achieved superior performance compared to the DLS, or the subjective model. The DLN is an end-to-end method and directly predicts the status of SSPNs. Abstract: Objective: To develop and validate a deep learning nomogram (DLN) model constructed from non-contrast computed tomography (CT) images for discriminating minimally invasive adenocarcinoma (MIA) from invasive adenocarcinoma (IAC) in patients with subsolid pulmonary nodules (SSPNs). Materials and Methods: In total, 365 consecutive patients who presented with SSPNs and were pathologically diagnosed with MIA or IAC after surgery, were recruited from two medical institutions from 2016 to 2019. Deep learning features were selected from preoperative CT images using convolutional neural network. Deep learning signature (DLS) was developed via the least absolute shrinkage and selection operator (LASSO). New DLN integrating clinical variables, subjective CT findings, and DLS was constructed. The diagnostic efficiency and discriminative capability were analyzed using the receiver operating characteristic method and decision curve analysis (DCA). Results: In total, 18 deep learning features with non-zero coefficients were enrolled to develop the DLS, which was statistically different between the MIA and IAC groups. Independent predictors of DLS and lobulated sharp were used to build the DLN. The areas under the curves of the DLN were 0.889 (95% confidence interval (CI): 0.824–0.936), 0.915 (95% CI: 0.846–0.959), and 0.914 (95% CI: 0.848–0.958) in the training, internal validation, and external validation cohorts, respectively. After stratification analysis and DCA, the DLN showed potential generalization ability. Conclusion: The DLN incorporating the DLS and subjective CT findings have strong potential to distinguish MIA from IAC in patients with SSPNs, and will facilitate the suitable treatment method selection for the management of SSPNs.